30 citations · 49 across the 8 of their papers we have counts for
12 papers
Hybrid Improved Document-level Embedding (HIDE)
Satanik Mitra, Mamata Jenamani
In recent times, word embeddings are taking a significant role in sentiment analysis. As the generation of word embeddings needs huge corpora, many applications use pretrained embe…
An Approximate Marginal Spread Computation Approach for the Budgeted Influence Maximization with Delay
Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar
In this paper, we study the Budgeted Influence Maximization with Delay Problem, for which the number of literature are limited. We propose an approximate marginal spread computatio…
Earned Benefit Maximization in Social Networks Under Budget Constraint
Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar
Given a social network with nonuniform selection cost of the users, the problem of \textit{Budgeted Influence Maximization} (BIM in short) asks for selecting a subset of the nodes…
Threshold-Based Heuristics for Trust Inference in a Social Network
Bithika Pal, Suman Banerjee, Mamata Jenamani
Trust among the users of a social network plays a pivotal role in item recommendation, particularly for the cold start users. Due to the sparse nature of these networks, trust info…
A Survey on Influence Maximization in a Social Network
Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar
Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in t…
Properties of a Projected Network of a Bipartite Network
Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar
Bipartite Graph is often a realistic model of complex networks where two different sets of entities are involved and relationship exist only two entities belonging to two different…
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray +2
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due t…
A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes
Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray +2
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir…
A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Soumi Chaki, Aurobinda Routray, William K. Mohanty +1
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classif…
Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
Soumi Chaki, Aurobinda Routray, William K. Mohanty +1
This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve…
Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
Soumi Chaki, Akhilesh K Verma, Aurobinda Routray +2
This paper proposes a complete framework consisting pre-processing, modeling, and post-processing stages to carry out well tops guided prediction of a reservoir property (sand frac…
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Akhilesh K Verma, Soumi Chaki, Aurobinda Routray +2
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency u…